Explore the vast range of titles available.

A new optimization technique is proposed for solving optimization problems having single and multiple objectives, with objective functions such as generation cost, loss, and severity value. This algorithm was developed to satisfy the constraints, such as OPF constraints, and practical constraints, such as ram rate limits. Single and multi-objective optimization problems were implemented with the proposed hybrid fruit fly-based artificial bee colony (HFABC) algorithm and the non-dominated sorting hybrid fruit fly-based artificial bee colony (NSHFABC) algorithm. HFABC is a hybrid model of the fruit fly and ABC algorithms. Selecting the user choice-based solution from the Pareto set by the proposed NSHFABC algorithm is performed by a fuzzy decision-based mechanism. The proposed HFABC method for single-objective optimization was analyzed using the Himmelblau test function, Booth’s test function, and IEEE 30 and IEEE 118 bus standard test systems. The proposed NSHFABC method for multi-objective optimization was analyzed using Schaffer1, Schaffer2, and Kursawe test functions, and the IEEE 30 bus test system. The obtained results of the proposed methods were compared with the existing literature.

The region of the isolated bubble regime, in which the bubbling starts, is a very significant process for boiling heat transfer. In this study, Artificial Bee Colony algorithm (ABC), which is mainly based on the searching optimum foods sources for the bees, has been used for the optimization of the pool boiling heat transfer calculation. The ABC algorithm is very handy for numerical analysis. The ABC algorithm has been compared with a genetic algorithm as well as other well-known correlation models for pool boiling heat transfer calculation. The ABC algorithm is found to be useful for any boundary conditions. The boundary conditions have been changed in order to improve the results. Results show that the ABC algorithm works faster than the genetic algorithm for the given problem. The ABC algorithm also predicts less average absolute error when compared with other well-known correlations as well as the optimization using the genetic algorithm.

Wireless Sensor Network (WSN) communication encounters security vulnerabilities, particularly with network traffic being susceptible to attacks during routing. The effective use of Deep Learning (DL) methods has been demonstrated in developing Intrusion Detection Systems (IDSs) to manage security attacks in Wireless Sensor Networks (WSN). Consequently, the development of new IDS becomes imperative, with DL and optimization algorithms offering superior attack detection capabilities. To address this need, we propose one new IDS by integrating Fuzzy Temporal rules and Artificial Bee Colony (ABC) optimization algorithm with Convolutional Neural Network (CNN) optimized with (FT-ABC-CNN) to enhance the classifier performance. To assess its effectiveness, a comparative analysis was conducted between the newly proposed FT-ABC-CNN algorithm and other classification algorithms commonly employed in Intrusion Detection System design, such as CNN, Long Short-Term Memory (LSTM), and Recurrent Neural Networks (RNN). Experimental evaluations revealed that the FT-ABC-CNN algorithm surpassed these comparable classifiers in terms of accuracy enhancement and reduction in false positive rates.

In this paper, an efficient face recognition method using AGA and ANFIS-ABC has been proposed. At first stage, the face images gathered from the database are preprocessed. At Second stage, an interest point which is used to improve the detection rate consequently. The parameters used in the interest point determination are optimized using the Adaptive Genetic Algorithm. Finally using ANFIS, face images are classified by using extracted features. During the training process, the parameters of ANFIS are optimized using Artificial Bee Colony Algorithm (ABC) in order to improve the accuracy. The performance of the proposed ANFIS-ABC technique is evaluated using an ORL database with 400 images of 40 individuals, YALE-B database with 165 images of 15 individuals and finally with real time video the detection rate and false alarm rate is compared with proposed and existing methods to prove the system efficiency.

This study considers an integrated process planning and scheduling (IPPS) problem for remanufacturing systems incorporating parallel disassembly workstations, a flexible job-shop-type reprocessing shop, and parallel reassembly workstations. This IPPS problem aims to determine the allocation/sequence of end-of-life products on the disassembly/reassembly shops and make decisions on the process path selection, operation sequencing, workstation allocation, and selection for reprocessing jobs. To solve the problem, a mixed-integer programming model is first built to characterize it mathematically, and a novel extended network graph is designed to represent and solve this problem visually. Then, an improved artificial bee colony algorithm is proposed that can solve the IPPS problem of remanufacturing systems with disassembly, reworking and reassembly shops simultaneously. In this introduced algorithm, a 3-level real-number solution representation scheme is adopted for encoding and decoding processes, and efficient neighborhood search structures are designed to improve the quality and diversity of the population. Computational experiments were systematically conducted on serval test instances. The results show that the proposed algorithm is highly advantageous for solving the IPPS problems in the remanufacturing systems by comparing it with four baseline algorithms.

The indirect and accurate determination of blast-induced rock movement has important significance in the reduction of ore loss and dilution and in the protection of environment. The present paper aims to predict blast-induced rock movement resulting from the Husab Uranium Mine, Namibia, the Coeur Rochester Mine, USA, and the Phoenix Mine, USA, and three new hybrid models using a genetic algorithm (GA), an artificial bee colony algorithm (ABC), a cuckoo search algorithm (CS) and support vector regression (SVR), namely the GA-SVR, ABC-SVR and CS-SVR models, are proposed. Eight typical blasting parameters rock type, number of free faces, first centerline distance, hole diameter, power factor, spacing, subdrill and initial depth of monitoring were chosen as the input variables to establish the intelligent model, and horizontal blast-induced rock movement (MH) was the output variable after conducting the available analyses of the database. Three performance metrics, including the correlation coefficient (R2), mean square error and variance account for, were used to assess the predictive performances of the aforementioned models. Based on the obtained results, the performance metrics show that the GA-SVR, ABC-SVR and CS-SVR model can provide satisfactory performance in estimating blast-induced rock movement, and GA-SVR model can achieve better results than the GWO-SVR, CS-SVR and ANN models when considering both predictive performance and calculation speed.Article HighlightsThree new hybrid predictive models are proposed (GA-SVR, ABC-SVR and CS-SVR).An more convenient, easily operable and higher accuracy predictive method for blast-induced rock movement determination is presented.The GA-SVR model can provide a higher performance capacity when considering both the predictive performance and the calculation speed.

In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

Artificial bee colony (ABC) algorithm is an efficient biological-inspired optimization method, which mimics the foraging behavior of honey bees to solve the complex and nonlinear optimization problems. However, in some cases, it suffers from inefficient exploration, low exploitation and slow convergence rate. These shortcomings cause the problem of stagnation at local optimum which is dangerous in determining the true solution (optima) of the problem. Therefore, in the present paper, an attempt has been made toward the removal of the drawbacks from the classical ABC by proposing a novel hybrid method called SCABC algorithm. The SCABC algorithm hybridizes the ABC with sine cosine algorithm (SCA) to upgrade the level of exploitation and exploration in the classical ABC algorithm. The SCA is a recently introduced algorithm, which uses the trigonometric functions sine and cosine to perform the search. The validation of the SCABC algorithm is performed on a well-known benchmark set of 23 optimization problems. The various analysis metrics such as statistical, convergence and performance index analysis verify the better search ability of the SCABC as compared to classical ABC, SCA. The comparison with some other optimization algorithms demonstrates a comparatively better state of exploitation and exploration in the SCABC algorithm. Moreover, the SCABC is also employed on multilevel thresholding problems. The various performance measures demonstrate the efficacy of the SCABC algorithm in determining the optimal thresholds of gray images.

The hybrid flowshop scheduling problem (HFSP), a typical NP-hard problem, has gained significant interest from researchers focusing on the development of solution methods. We focus on a variable speed hybrid flowshop scheduling problem. We assume that machines operate at variable speed when processing workpieces, making the problem more reflective of real-world scenarios. Aiming at this problem, a speed optimization strategy for encoding and decoding is proposed. Meanwhile, we design a constructive-destructive search driven artificial bee colony algorithm to solve the variable-speed green hybrid flow shop scheduling problem to minimize the makespan and total energy consumption. A constructive-destructive neighbor search method is designed to update population search in the employed bee phase. The search process is redesigned with three operators named the technique of order preferences for similarity of ideal solutions, binary tournament selection, and global update strategies in the onlooker bee phase. In the scout bee phase, individual evaluation and replacement strategies are designed. Extensive experimental evaluations testify that the CDSABC outperforms other algorithms regarding the best, worst, average, and standard deviation of the IGD index in 80% of the test cases.

In this paper, we propose efficient methods for the reconstruction, compression, compressive sensing (CS) and encryption of 1D signals. The proposed reconstruction method is based on the use of Charlier moments (CMs) and the Artificial Bee Colony (ABC) algorithm. The latter is used for optimizing the local parameter of Charlier polynomials during the computation of CMs. In addition, new methods are presented for 1D signal compression and CS using CMs and ABC algorithm that guarantees a high quality of the decompressed/reconstructed signal. Moreover, we suggest a new signal encryption/decryption scheme relying on fractional-order Charlier moments and ABC algorithm, which is used for providing a high quality of the decrypted signal and for improving the security of the proposed scheme. The results of the conducted simulations and comparisons clearly show the efficiency of the proposed 1D-signal analysis methods.